The Application of Improved PSO Algorithm in the Geometric Constraint Solving

Geometric constraint solving is a hot topic in the constraint design research field. Particle swarm optimization (PSO) is a method to solve the optimization problem from the biological population’s behavior characteristics. PSO is easy to diverge and fall into the local optimum. There are various kinds of improvements. In addition to improving some performance, the corresponding cost is paid. In this paper, a particle swarm optimization algorithm based on the geese is adopted to solve the geometric constraint problem. The algorithm is inspired by the flight characteristics of geese; each particle follows the optimal particle in front of it to keep the diversity; each particle can share more useful information of other particles, which strengthens cooperation and competition between particles. The algorithm balances the contradiction between the search speed and the accuracy of the algorithm to a certain extent. Experimental results show that the proposed algorithm can improve the efficiency and convergence of geometric constraint solving.